SoroushVahidi/scidocs huggingface dataset:
Design and Control of Autonomous Underwater Robots: A Survey
(2000)

During the 1990s, numerous worldwide research and development activities have occurred in underwater robotics, especially in the area of autonomous underwater vehicles (AUVs). As the ocean attracts great attention on environmental issues and resources as well as scientific and military tasks, the need for and use of underwater robotic systems has become more apparent. Great efforts have been made in developing AUVs to overcome challenging scientific and engineering problems caused by the unstructured and hazardous ocean environment. In the 1990s, about 30 new AUVs have been built worldwide. With the development of new materials, advanced computing and sensory technology, as well as theoretical advancements, R&D activities in the AUV community have increased. However, this is just the beginning for more advanced, yet practical and reliable AUVs. This paper surveys some key areas in current state-of-the-art underwater robotic technologies. It is by no means a complete survey but provides key references for future development. The new millennium will bring advancements in technology that will enable the development of more practical, reliable AUVs.
Logic Tensor Networks: Deep Learning and Logical Reasoning from Data and Knowledge
(2016)

We propose Logic Tensor Networks: a uniform framework for integrating automatic learning and reasoning. A logic formalism called Real Logic is defined on a first-order language whereby formulas have truth-value in the interval [0,1] and semantics defined concretely on the domain of real numbers. Logical constants are interpreted as feature vectors of real numbers. Real Logic promotes a well-founded integration of deductive reasoning on a knowledge-base and efficient data-driven relational machine learning. We show how Real Logic can be implemented in deep Tensor Neural Networks with the use of Google’s TENSORFLOW primitives. The paper concludes with experiments applying Logic Tensor Networks on a simple but representative example of knowledge comple-
Survey on Data Classification and Data Encryption Techniques Used in Cloud Computing
(2016)

Cloud computing is an imminent revolution in information technology (IT) industry because of its performance, accessibility, low cost and many other luxury features. Security of data in the cloud is one of the major issues which acts as barrier in the implementation of cloud computing. In past years, a number of research works have targeted this problem. In this paper discuss some of the data classification techniques widely used in cloud computing. The objective of data classification is to find out the required level of security for data and to protect data by providing sufficient level of security according to the risk levels of data. In this paper also discuss a survey of existing solutions for security problem, discuss their advantages, and point out any disadvantages for future research. Specifically, focus on the use of encryption techniques, and provide a comparative study of the major encryption techniques.
Metaphor in the Mind : The Cognition of Metaphor
(2006)

The most sustained and innovative recent work on metaphor has occurred in cognitive science and psychology. Psycholinguistic investigation suggests that novel, poetic metaphors are processed differently than literal speech, while relatively conventionalized and contextually salient metaphors are processed more like literal speech. This conflicts with the view of “cognitive linguists” like George Lakoff that all or nearly all thought is essentially metaphorical. There are currently four main cognitive models of metaphor comprehension: juxtaposition, category-transfer, feature-matching, and structural alignment. Structural alignment deals best with the widest range of examples; but it still fails to account for the complexity and richness of fairly novel, poetic metaphors. 1. General Issues in the Study of Metaphor Philosophers have often adopted a dismissive attitude toward metaphor. Hobbes (ch. 8) advocated excluding metaphors from rational discourse because they “openly profess deceit,” while Locke (Bk. 3, ch. 10) claimed that figurative uses of language serve only “to insinuate wrong ideas, move the passions, and thereby mislead the judgment; and so indeed are perfect cheats.” Later, logical positivists like Ayer and Carnap assumed that because metaphors like (1) How sweet the moonlight sleeps upon this bank!2 involve category mistakes, they have no real meaning or verification conditions. Thus, they too mentioned metaphor only to place it beyond the pale of rational discourse. Starting in the 1960s and 70s, philosophers and linguists began to take more positive interest in metaphor. Black argued forcefully that metaphors do have a distinctive, essentially non-propositional meaning or cognitive significance, which is produced by the “interaction” of the “systems of associated commonplaces” for the metaphor’s “primary” and “secondary” subjects (e.g., with moonlight and sleeping sweetly). Other theorists were more friendly to the idea that metaphorical and literal meaning are of the same essential kind. Many of them proposed that the literal absurdity of metaphors © Blackwell Publishing 2006 Philosophy Compass 1/2 (2006): 154–170, 10.1111/j.1747-9991.2006.00013.x
Faculty at Saudi Electronic University attitudes toward using augmented reality in education
(2018)

This study aims to examine the possibility of implementing the Augmented Reality (AR) application in higher education by answering three questions: Which extended faculty at the Saudi Electronic University are familiar with such applications, what the perceptions do they hold toward using it in education, and what barriers do they believe may hinder implementing this technology. An online survey was designed and distributed to faculty members at two colleges selected randomly to collect data from participants. Even though the faculty were at an accepted level of familiarity with this technology, they did not use it in their classes. Results showed that faculty have a positive attitude toward the use of AR and have confidence in its potential to enrich the learning environment. This result is connected to the AR advantages the faculty were in agreement with. Results also showed that faculty have concerns about some barriers that might impact implementation of AR in the education environment, such as the lack of technical support.
Impact of supply chain integration and selective disintegration on supply chain efficiency and organization performance
(2016)

An efficient business system integration (BSI) is the key determinant factor for the organization to stay competitive. The goal of this study is to develop and execute a research project to study BSI in the oil & gas industry and how an efficient BSI in supply chain (SCI) can be used to gain advantage. A mixed (qualitative & quantitative) survey method was employed and structural equation modeling (SEM) is applied to test the theoretical framework and hypotheses. Findings reveal that, total integration do not offer optimum performance and selective disintegration (DIS) is necessary for supply chain efficiency (SCE) and organization performance (PER). This study helps to better understand the degree of integration required for optimum supply chain performance and came up with new construct DIS. Specifically, this study investigated the effect of SCI and DIS on SCE and PER. The compelled sample size precludes evaluating and testing of additional broad models of the relationship amongst the constructs. The main managerial lesson is that, in contrast to what has been written in many books and other popular publications, selective disintegration is necessary for optimum supply chain efficiency.
Learning to summarize web image and text mutually
(2012)

We consider the problem of learning to summarize images by text and visualize text utilizing images, which we call Mutual-Summarization. We divide the web image-text data space into three subspaces, namely pure image space (PIS), pure text space (PTS) and image-text joint space (ITJS). Naturally, we treat the ITJS as a knowledge base.
 For summarizing images by sentence issue, we map images from PIS to ITJS via image classification models and use text summarization on the corresponding texts in ITJS to summarize images. For text visualization problem, we map texts from PTS to ITJS via text categorization models and generate the visualization by choosing the semantic related images from ITJS, where the selected images are ranked by their confidence. In above approaches images are represented by color histograms, dense visual words and feature descriptors at different levels of spatial pyramid; and the texts are generated according to the Latent Dirichlet Allocation (LDA) topic model. Multiple Kernel (MK) methodologies are used to learn classifiers for image and text respectively. We show the Mutual-Summarization results on our newly collected dataset of six big events ("Gulf Oil Spill", "Haiti Earthquake", etc.) as well as demonstrate improved cross-media retrieval performance over existing methods in terms of MAP, Precision and Recall.
A-PNR: Automatic Plate Number Recognition
(2017)

Automatic Plate Number Recognition (APNR) has important applications in traffic surveillance, toll booth, protected parking lot, No parking zone, etc. It is a challenging problem, especially when the license plates have varying sizes, the number of lines, fonts, background diversity etc. This work aims to address APNR using deep learning method for real-time traffic images. We first extract license plate candidates from each frame using edge information and geometrical properties, ensuring high recall using one class SVM. The verified candidates are used for NP recognition purpose along with a spatial transformer network (STN) for character recognition. Our system is evaluated on several traffic images with vehicles having different license plate formats in terms of tilt, distances, colors, illumination, character size, thickness etc. Also, the background was very challenging. Results demonstrate robustness to such variations and impressive performance in both the localization and recognition.
Virtual reality job interview training in adults with autism spectrum disorder.
(2014)

The feasibility and efficacy of virtual reality job interview training (VR-JIT) was assessed in a single-blinded randomized controlled trial. Adults with autism spectrum disorder were randomized to VR-JIT (n = 16) or treatment-as-usual (TAU) (n = 10) groups. VR-JIT consisted of simulated job interviews with a virtual character and didactic training. Participants attended 90 % of laboratory-based training sessions, found VR-JIT easy to use and enjoyable, and they felt prepared for future interviews. VR-JIT participants had greater improvement during live standardized job interview role-play performances than TAU participants (p = 0.046). A similar pattern was observed for self-reported self-confidence at a trend level (p = 0.060). VR-JIT simulation performance scores increased over time (R(2) = 0.83). Results indicate preliminary support for the feasibility and efficacy of VR-JIT, which can be administered using computer software or via the internet.
Event-based stock market prediction
(2014)

There are various studies on the behavior of the market. In particular, derivatives such as futures and options have taken a lot of attentions, lately. Predicting these derivatives is not only important for the risk management purposes, but also for price speculative activities. Besides that accurate prediction of the market’s direction can help investors to gain enormous profits with small amount of capital [Tsaih et al., 1998]. Stock market prediction can be viewed as a challenging time-series prediction [Kim, 2003]. There are many factors that are influential on the financial markets, including political events, natural disasters, economic conditions, and so on. Despite the complexity of the movements in market prices, the market behavior is not completely random. Instead, it is governed by a highly nonlinear dynamical system [Blank, 1991]. Forecasting the future prices is carried out based on the technical analysis, which studies the market’s action using past prices and the other market information. Market analysis is in contradiction with the Efficient Market Hypothesis (EMH). EMH was developed in 1970 by economist Eugene Fama [Fama, 1965a, Fama, 1965b] whose theory stated that it is not possible for an investor to outperform the market because all available information is already built into all stock prices. If the EMH was true, it would not be possible to use machine learning techniques for market prediction. Nevertheless, there are many successful technical analysis in the financial world and number of studies appearing in academic literature that are using machine learning techniques for market prediction [Choudhry and Garg, 2008]. One way to forecast the market movement is by analyzing the special events of the market such as earnings announcements. Earnings announcement for each stock is an official public statement of a company’s profitability for a specific time period, typically a quarter or a year. Each company has its specific earnings announcement dates. Stock price of a company is affected by the earnings announcement event. Equity analysts usually predict the earnings per share (EPS) prior to the announcement date. In this project, using machine-learning techniques, we want to predict whether a given stock will be rising in the following day after earnings announcement or not. This will lead to a binary classification problem, which can be tackled based on the huge amount of available public data. This project consists of two major tasks: data collection and application of machine learning algorithms. In §2, we will discuss our efforts to collect the required data. In continuation in §3, features definitions and selections are described. We have considered and discussed different machine learning algorithms in §4. In §5, the summary of our results and possible extensions are explained.
Effects of early-life abuse differ across development: infant social behavior deficits are followed by adolescent depressive-like behaviors mediated by the amygdala.
(2012)

Abuse during early life, especially from the caregiver, increases vulnerability to develop later-life psychopathologies such as depression. Although signs of depression are typically not expressed until later life, signs of dysfunctional social behavior have been found earlier. How infant abuse alters the trajectory of brain development to produce pathways to pathology is not completely understood. Here we address this question using two different but complementary rat models of early-life abuse from postnatal day 8 (P8) to P12: a naturalistic paradigm, where the mother is provided with insufficient bedding for nest building; and a more controlled paradigm, where infants undergo olfactory classical conditioning. Amygdala neural assessment (c-Fos), as well as social behavior and forced swim tests were performed at preweaning (P20) and adolescence (P45). Our results show that both models of early-life abuse induce deficits in social behavior, even during the preweaning period; however, depressive-like behaviors were observed only during adolescence. Adolescent depressive-like behavior corresponds with an increase in amygdala neural activity in response to forced swim test. A causal relationship between the amygdala and depressive-like behavior was suggested through amygdala temporary deactivation (muscimol infusions), which rescued the depressive-like behavior in the forced swim test. Our results indicate that social behavior deficits in infancy could serve as an early marker for later psychopathology. Moreover, the implication of the amygdala in the ontogeny of depressive-like behaviors in infant abused animals is an important step toward understanding the underlying mechanisms of later-life mental disease associated with early-life abuse.
Neural correlates of cognitive processing in monolinguals and bilinguals.
(2017)

Here, we review the neural correlates of cognitive control associated with bilingualism. We demonstrate that lifelong practice managing two languages orchestrates global changes to both the structure and function of the brain. Compared with monolinguals, bilinguals generally show greater gray matter volume, especially in perceptual/motor regions, greater white matter integrity, and greater functional connectivity between gray matter regions. These changes complement electroencephalography findings showing that bilinguals devote neural resources earlier than monolinguals. Parallel functional findings emerge from the functional magnetic resonance imaging literature: bilinguals show reduced frontal activity, suggesting that they do not need to rely on top-down mechanisms to the same extent as monolinguals. This shift for bilinguals to rely more on subcortical/posterior regions, which we term the bilingual anterior-to-posterior and subcortical shift (BAPSS), fits with results from cognitive aging studies and helps to explain why bilinguals experience cognitive decline at later stages of development than monolinguals.
Simple sequence repeat markers that identify Claviceps species and strains
(2016)

Claviceps purpurea is a pathogen that infects most members of Pooideae, a subfamily of Poaceae, and causes ergot, a floral disease in which the ovary is replaced with a sclerotium. When the ergot body is accidently consumed by either man or animal in high enough quantities, there is extreme pain, limb loss and sometimes death. This study was initiated to develop simple sequence repeat (SSRs) markers for rapid identification of C. purpurea. SSRs were designed from sequence data stored at the National Center for Biotechnology Information database. The study consisted of 74 ergot isolates, from four different host species, Lolium perenne, Poa pratensis, Bromus inermis, and Secale cereale plus three additional Claviceps species, C. pusilla, C. paspali and C. fusiformis. Samples were collected from six different counties in Oregon and Washington over a 5-year period. Thirty-four SSR markers were selected, which enabled the differentiation of each isolate from one another based solely on their molecular fingerprints. Discriminant analysis of principle components was used to identify four isolate groups, CA Group 1, 2, 3, and 4, for subsequent cluster and molecular variance analyses. CA Group 1 consisting of eight isolates from the host species P. pratensis, was separated on the cluster analysis plot from the remaining three groups and this group was later identified as C. humidiphila. The other three groups were distinct from one another, but closely related. These three groups contained samples from all four of the host species. These SSRs are simple to use, reliable and allowed clear differentiation of C. humidiphila from C. purpurea. Isolates from the three separate species, C. pusilla, C. paspali and C. fusiformis, also amplified with these markers. The SSR markers developed in this study will be helpful in defining the population structure and genetics of Claviceps strains. They will also provide valuable tools for plant breeders needing to identify resistance in crops or for researchers examining fungal movements across environments.
Lifetime-Aware Scheduling and Power Control for M2M Communications in LTE Networks
(2015)

In this paper the scheduling and transmit power control are investigated to minimize the energy consumption for battery-driven devices deployed in LTE networks. To enable efficient scheduling for a massive number of machine-type subscribers, a novel distributed scheme is proposed to let machine nodes form local clusters and communicate with the base-station through the cluster-heads. Then, uplink scheduling and power control in LTE networks are introduced and lifetime-aware solutions are investigated to be used for the communication between cluster-heads and the base-station. Beside the exact solutions, low-complexity suboptimal solutions are presented in this work which can achieve near optimal performance with much lower computational complexity. The performance evaluation shows that the network lifetime is significantly extended using the proposed protocols.
Rumor detection for Persian Tweets
(2017)

Nowadays, striking growth of online social media has led to easier and faster spreading of rumors on cyber space, in addition to tradition ways. In this paper, rumor detection on Persian Twitter community has been addressed for the first time by exploring and analyzing the significances of two categories of rumor features: Structural and Content-based features. While applying both feature sets leads to precision more than 80%, precision around 70% using only structural features has been obtained. Moreover, we show how features of users tending to produce and spread rumors are effective in rumor detection process. Also, the experiments have led to learning of a language model of collected rumors. Finally, all features have been ranked and the most discriminative ones have been discussed.
Covalsa: Covariance estimation from compressive measurements using alternating minimization
(2014)

The estimation of covariance matrices from compressive measurements has recently attracted considerable research efforts in various fields of science and engineering. Owing to the small number of observations, the estimation of the covariance matrices is a severely ill-posed problem. This can be overcome by exploiting prior information about the structure of the covariance matrix. This paper presents a class of convex formulations and respective solutions to the high-dimensional covariance matrix estimation problem under compressive measurements, imposing either Toeplitz, sparseness, null-pattern, low rank, or low permuted rank structure on the solution, in addition to positive semi-definiteness. To solve the optimization problems, we introduce the Co-Variance by Augmented Lagrangian Shrinkage Algorithm (CoVALSA), which is an instance of the Split Augmented Lagrangian Shrinkage Algorithm (SALSA). We illustrate the effectiveness of our approach in comparison with state-of-the-art algorithms.
Control Strategy to Naturally Balance Hybrid Converter for Variable-Speed Medium-Voltage Drive Applications
(2015)

In this paper, a novel control strategy to naturally balance a hybrid converter is presented for medium-voltage induction motor drive applications. Fundamental current of the motor is produced by a three-level quasi-square-wave converter. Several single-phase full-bridge converters are connected in series with this quasi-square-wave converter to eliminate harmonic voltages from the net output voltage of this hybrid converter. Various novel modulation strategies for these series cells are proposed. These lead to a naturally regulated dc-bus voltage of series cells. These also optimize the number of series cells required. Variable fundamental voltage and frequency control of induction motor is adopted with a constant dc-bus voltage of the three-level quasi-square-wave converter. Experimental results are also produced here to validate the proposed modulation strategies for induction motor drives using low-voltage protomodel. Based on the aforementioned, a 2.2-kV induction motor drive has been built, and results are also produced in this paper.